Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi !

Side-Channel Analysis and Machine Learning: A Practical Perspective (CROSBI ID 648189)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Picek, Stjepan ; Heuser, Annelie ; Jović, Alan ; Ludwig, Simone A. ; Guilley, Sylvain ; Jakobović, Domagoj ; Mentens, Nele Side-Channel Analysis and Machine Learning: A Practical Perspective // Proceedings of the International Joint Conference on Neural Networks. Anchorage (AK): The Printing House, Inc, IEEE, 2017. str. 4095-4102

Podaci o odgovornosti

Picek, Stjepan ; Heuser, Annelie ; Jović, Alan ; Ludwig, Simone A. ; Guilley, Sylvain ; Jakobović, Domagoj ; Mentens, Nele

engleski

Side-Channel Analysis and Machine Learning: A Practical Perspective

The field of side-channel analysis has made significant progress over time. Side-channel analysis is now used in practice in design companies as well as in test laboratories, and the security of products against side-channel attacks has significantly improved. However, there are still some remaining issues to be solved for side-channel analysis to become more effective. Side-channel analysis consists of two steps, commonly referred to as identification and exploitation. The identification consists of understanding the leakage and building suitable models. The exploitation consists of using the identified leakage models to extract the secret key. In scenarios where the model is poorly known, it can be approximated in a profiling phase. There, machine learning techniques are gaining value. In this paper, we conduct extensive analysis of several machine learning techniques, showing the importance of proper parameter tuning and training. In contrast to what is perceived as common knowledge in unrestricted scenarios, we show that some machine learning techniques can significantly outperform template attacks when properly used. We therefore stress that the traditional worst case security assessment of cryptographic implementations, that mainly includes template attacks, might not be accurate enough. Besides that, we present a new measure called the Data Confusion Factor that can be used to assess how well machine learning techniques will perform on a certain dataset.

side-channel analysis ; machine learning techniques ; profiling ; parameter tuning ; data mining

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

Podaci o prilogu

4095-4102.

2017.

objavljeno

Podaci o matičnoj publikaciji

Proceedings of the International Joint Conference on Neural Networks

Anchorage (AK): The Printing House, Inc, IEEE

978-1-5090-6181-5

Podaci o skupu

IEEE International Joint Conference on Neural Networks (IJCNN)

predavanje

14.05.2017-19.05.2017

Anchorage (AK), Sjedinjene Američke Države

Povezanost rada

Računarstvo